Bibliographic record
Abstract
Introduction This chapter is co-written by two people committed to adding the voice of community partners to the debate about the value of community–university research partnerships (CUPs). Our focus is on research partnerships and we are using the CUP acronym as shorthand. Within the UK there have been strong developments in supporting more sustained partnerships between universities and communities. The organisations we represent are at the forefront of these initiatives. Kim Aumann is a community practitioner with 12 years’ experience of CUP working in the UK and abroad, and was involved in setting up the UK Community Partner Network (UKCPN), and Sophie Duncan is Deputy Director of the National Co-ordinating Centre for Public Engagement (NCCPE), which works to support universities to engage more effectively with the public, including providing international consultancy services and hosting the UKCPN. We believe that tapping into the experience and perspectives of community partners can help us improve the benefits of CUP working (Aumann, Duncan and Hart, 2014). Our work is internationally linked, while having a UK focus. To proactively explore how to encourage, support and facilitate effective use of CUPs to benefit society, between us we have conducted focused consultations and events with community partners (and academics); met and spoken with hundreds of community partners both in the UK and internationally; participated in international networks, including Community Campus Partnerships for Health (CCPH), the Living Knowledge Network, Global University Network for Innovation, Community Based Research Canada, UNESCO Chairs for Community Based Research, and Global Alliance for Community Engaged Research; worked with research funders; and facilitated national CUP projects. It is this direct experience and our reflections on practice that we draw on here. Our chapter is addressed to community partners and academics interested in CUP working. We are convinced that this way of working can produce more than individual partners can achieve on their own. However, we are conscious of the need to get better at articulating and evidencing the value and legacies of these ways of working if we are going to help protect the future of this form of knowledge creation and use. It is important that CUPs are an effective and appropriate use of public funding and that they do what they say they will do in a way that inspires confidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".